On the need of trustworthy sensing and crowdsourcing for urban accessibility in smart city

Mobility in urban environments is an undisputed key factor that can affect citizens' well-being and quality of life. This is particularly relevant for those people with disabilities or with reduced mobility who have to face the presence of barriers in urban areas. In this scenario, the availability of information about such architectural elements (together with facilities) can greatly support citizens' mobility by enhancing their independence and their abilities in conducting daily outdoor activities. With this in mind, we have designed and developed mobile Pervasive Accessibility Social Sensing (mPASS), a system that provides users with personalized paths, computed on the basis of their own preferences and needs, with a customizable and accessible interface. The system collects data from crowdsourcing and crowdsensing to map urban and architectural accessibility by providing reliable information coming from different data sources with different levels of trustworthiness. In this context, reliability can be ensured by properly managing crowdsourced and crowdsensed data, combined when possible with authoritative datasets, provided by disability rights organizations and local authorities. To demonstrate this claim, in this article we present our trustworthiness model and discuss results we have obtained by simulations.